Abstract: Sleep disorders, such as Insomnia, Sleep Apnea, and other conditions, significantly impact individuals' health and well-being. Accurate and efficient classification of these disorders can aid in early diagnosis and effective treatment, enhancing the quality of life for affected individuals. The existing systems predominantly rely on Artificial Neural Networks (ANN) for classification, which, while effective, can be computationally intensive and less interpretable. This study proposes a Random Forest-based approach for classifying sleep disorders, utilizing a dataset consisting of 400 samples with 13 relevant features. Random Forest model was selected for its robustness, interpretability, and superior ability to handle complex, non-linear relationships within the data. By employing this algorithm, the study aims to classify sleep disorders into three classes: Insomnia, None, and Sleep Apnea, demonstrating improved performance compared to traditional ANN-based systems. The evaluation of the Random Forest model is conducted using standard performance metrics, including accuracy, precision, recall, and F1-score, which show that the proposed approach outperforms existing models, offering enhanced accuracy and reliability in the classification of sleep disorders.

| DOI: 10.17148/IJIREEICE.2025.13703